My Other Computer Is a Supercomputer
By late December 2002, with the last of the hardware and software issues resolved, we turned our sites toward putting Iceberg on the TOP500 Supercomputer list (www.top500.org). The TOP500 is a biyearly competition that ranks 500 entries by sustained performance for a linear equation solver, Linpack. On the November 2002 list, there were 97 commodity clusters, so we felt confident we could put Iceberg on the list.
Taking a run at the TOP500 list proved to be more work than we anticipated. Rocks comes with a prebuilt Linpack executable that can attain good performance on a Pentium 4 cluster, but I wanted more. My account representative put me in contact with the Scalable Systems Group at Dell. We collaborated on tuning Linpack on the cluster and did work such as linking Linpack against the Goto BLAS (basic linear algebra subroutines) library written by Kazushige Goto (www.cs.utexas.edu/users/flame/goto). Additionally, Dell suggested an improved interconnect topology. Prior to the TOP500 run, all 300 nodes were distributed over 16 100Mbit Ethernet switches (Dell PowerConnect 3024). We found that Linpack, like many highly parallel applications, benefits from an improved network interconnect (in other words, one with lower latency and/or higher bandwidth). Dell loaned us a Gigabit nonblocking switch to replace some of our 100Mbit switches.
The above enhancements improved our performance, and we submitted the results for the June 2003 TOP500 list. Iceberg sits at #319 and, in my estimation, could be higher with a faster interconnect.
From the beginning, Iceberg's permanent home was to be in the James H. Clark Center, which is named for the man who started Silicon Graphics and Netscape and who is the predominant funding source for the Bio-X Project. By August 2003, it was time to move. The move from the Forsythe Data Center brought many great things, one of which was a clean installation of Rocks. I really pushed for this because having a solid and stable infrastructure is key in maintaining a cluster of this size while maintaining a low total cost of ownership.
Through the lifetime of Iceberg at the Forsythe Data Center, we found that both choices on the configuration of software and hardware could be improved. The downtime incurred during the move allowed us to make modifications to the physical design. We decided to go with a front-end node and move the home directories to another node with attached storage. Once again, Rocks came through. It was as simple as using insert-ethers and selecting NAS appliance as the type of node being inserted. We chose to use link aggregation to exploit the dual Gigabit Ethernet network cards in the NAS appliance fully. After a few modifications on the front-end node to connect users to the new appliance and moving the backed-up data to the new appliance, we were operational once again.
Folding@home on Iceberg
We use Iceberg as a debug platform for Folding@home, which is a distributed computing project to study protein folding, misfolding, aggregation and related diseases. Volunteers contribute spare processing time to the project, and currently about 80,000 CPUs are active.
For the Folding@home research study, I exclusively use Iceberg to simulate small projects, where a project is a set of simulations of one protein coupled with a specific method. Writing a script that mimics what Folding@home does with clients was the key to this work. For a run, typically I use 10–20 CPUs at a time.
For other large projects, I used Iceberg for the starting portion only. We usually calculate 10–50ns of simulations in chunks of 1ns. I could use Iceberg for the first 1ns and then move it to Folding@home and continue there. We can rapidly iterate upon new methods in the controlled, stable environment that Iceberg presents. As we develop new projects, we use Iceberg to validate the results, and once we are confident with the new methods, we unleash the new project onto the 80,000-CPU distributed computer.
—Young Min Rhee of the Folding@home Project, folding.stanford.edu
|Comprehensive Identity Management and Audit for Red Hat Enterprise Linux||Jun 29, 2015|
|Linux Kernel 4.1 Released||Jun 26, 2015|
|Secure Server Deployments in Hostile Territory||Jun 25, 2015|
|Take Control of Growing Redis NoSQL Server Clusters||Jun 24, 2015|
|Django Templates||Jun 24, 2015|
|Attack of the Drones||Jun 23, 2015|
- Comprehensive Identity Management and Audit for Red Hat Enterprise Linux
- Secure Server Deployments in Hostile Territory
- Linux Kernel 4.1 Released
- Django Templates
- Cinnamon 2.6 Released
- Gettin' Sticky with It
- Take Control of Growing Redis NoSQL Server Clusters
- Attack of the Drones
- diff -u: What's New in Kernel Development
- Physics Analysis Workstation